DBMS > HarperDB vs. Microsoft Azure Cosmos DB vs. RavenDB vs. XTDB

System Properties Comparison HarperDB vs. Microsoft Azure Cosmos DB vs. RavenDB vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Globally distributed, horizontally scalable, multi-model database service

Open Source Operational and Transactional Enterprise NoSQL Document Database

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Secondary database models

Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

www.harperdb.io

azure.microsoft.com/services/cosmos-db

ravendb.net

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.harperdb.io/docs

learn.microsoft.com/azure/cosmos-db

ravendb.net/docs

www.xtdb.com/docs

Developer

HarperDB

Microsoft

Hibernating Rhinos

Juxt Ltd.

Initial release

2017

2014

2010

2019

Current release

3.1, August 2021

5.4, July 2022

1.19, September 2021

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

AGPL version 3, commercial license available

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Node.js

Clojure

Server operating systems

Linux
OS X

hosted

Linux
macOS
Raspberry Pi
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

dynamic schema

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

yes

JSON types

yes, extensible-data-notation format

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like data manipulation statements

SQL-like query language

SQL-like query language (RQL)

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

HTTP REST
JDBC

Supported programming languages

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Clojure
Java

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

JavaScript

yes

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

A table resides as a whole on one (or more) nodes in a cluster

Sharding

Implicit feature of the cloud service

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

yes

Implicit feature of the cloud service

Multi-source replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Atomic execution of specific operations

Multi-item ACID transactions with snapshot isolation within a partition

ACID, Cluster-wide transaction available

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes, using LMDB

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access rights for users and roles

Access rights can be defined down to the item level

Authorization levels configured per client per database

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